NIPS 2017: Long Beach, CA, USA
Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA. 2017
Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber:
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. 1-11
Constantinos Daskalakis, Nishanth Dikkala, Gautam C. Kamath:
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. 12-22
Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian D. Reid:
Deep Subspace Clustering Networks. 23-32

Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. 55-64
Jian Zhao, Lin Xiong, Jayashree Karlekar, Jianshu Li, Fang Zhao, Zhecan Wang, Sugiri Pranata, Shengmei Shen, Shuicheng Yan, Jiashi Feng:
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis. 65-75
Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael J. Witbrock, Mark A. Hasegawa-Johnson, Thomas S. Huang:
Dilated Recurrent Neural Networks. 76-86
Saurabh Verma, Zhi-Li Zhang:
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs. 87-97
Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett:
Scalable Generalized Linear Bandits: Online Computation and Hashing. 98-108
Chris J. Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark A. Girolami:
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. 109-117
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer:
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. 118-128
El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer:
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning. 129-139
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum:
Learning to See Physics via Visual De-animation. 152-163
Zelun Luo, Yuliang Zou, Judy Hoffman, Fei-Fei Li:
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks. 164-176
Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu:
Decoding with Value Networks for Neural Machine Translation. 177-186
Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao:
Parametric Simplex Method for Sparse Learning. 187-196

Krzysztof Marcin Choromanski, Mark Rowland, Adrian Weller:
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. 218-227
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, Adrian Weller:
From Parity to Preference-based Notions of Fairness in Classification. 228-238
Paroma Varma, Bryan D. He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel L. Rubin, Christopher Ré:
Inferring Generative Model Structure with Static Analysis. 239-249
Maja R. Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. 250-260
Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. 261-270
Rui Ponte Costa, Ioannis Alexandros M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels:
Cortical microcircuits as gated-recurrent neural networks. 271-282
Cong Han Lim, Stephen J. Wright:
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms. 283-291
Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. 301-312
Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra:
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model. 313-323
Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing:
MaskRNN: Instance Level Video Object Segmentation. 324-333

Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang:
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks. 353-363

Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang:
Universal Style Transfer via Feature Transforms. 385-395
Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura:
On the Model Shrinkage Effect of Gamma Process Edge Partition Models. 396-404
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool:
Pose Guided Person Image Generation. 405-415
Murat A. Erdogdu, Yash Deshpande, Andrea Montanari:
Inference in Graphical Models via Semidefinite Programming Hierarchies. 416-424
Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar:
Variable Importance Using Decision Trees. 425-434
Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura:
Preventing Gradient Explosions in Gated Recurrent Units. 435-444
Simon S. Du, Yining Wang, Aarti Singh:
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. 445-455
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. 456-464
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman:
Toward Multimodal Image-to-Image Translation. 465-476
Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen M. Chu:
Mixture-Rank Matrix Approximation for Collaborative Filtering. 477-485
Andrew An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. 486-496
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi:
Learning multiple visual domains with residual adapters. 506-516
Ryan J. Tibshirani:
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. 517-528
Yu-Chuan Su, Kristen Grauman:
Learning Spherical Convolution for Fast Features from 360° Imagery. 529-539
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum:
MarrNet: 3D Shape Reconstruction via 2.5D Sketches. 540-550
Ilija Ilievski, Jiashi Feng:
Multimodal Learning and Reasoning for Visual Question Answering. 551-562
Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart:
Adversarial Surrogate Losses for Ordinal Regression. 563-573
Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Hypothesis Transfer Learning via Transformation Functions. 574-584
Qizhe Xie, Zihang Dai, Yulun Du, Eduard H. Hovy, Graham Neubig:
Controllable Invariance through Adversarial Feature Learning. 585-596
Yuanzhi Li, Yang Yuan:
Convergence Analysis of Two-layer Neural Networks with ReLU Activation. 597-607
Tomoya Murata, Taiji Suzuki:
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization. 608-617
Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk:
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. 618-626
Shixiang Chen, Shiqian Ma, Wei Liu:
Geometric Descent Method for Convex Composite Minimization. 636-644
Chris Junchi Li, Mengdi Wang, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. 645-655
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. 656-666
Ilja Kuzborskij, Nicolò Cesa-Bianchi:
Nonparametric Online Regression while Learning the Metric. 667-676
Novi Quadrianto, Viktoriia Sharmanska:
Recycling Privileged Learning and Distribution Matching for Fairness. 677-688
Noam Brown, Tuomas Sandholm:
Safe and Nested Subgame Solving for Imperfect-Information Games. 689-699

Zhaobin Kuang, Sinong Geng, David Page:
A Screening Rule for l1-Regularized Ising Model Estimation. 720-731
Lijun Zhang, Tianbao Yang, Jinfeng Yi, Jing Rong, Zhi-Hua Zhou:
Improved Dynamic Regret for Non-degenerate Functions. 732-741
Guobin Chen, Wongun Choi, Xiang Yu, Tony X. Han, Manmohan Chandraker:
Learning Efficient Object Detection Models with Knowledge Distillation. 742-751
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin:
Deep Mean-Shift Priors for Image Restoration. 763-772
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. 773-784
Jeremiah Liu, Brent Coull:
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. 795-803
Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys:
Lower bounds on the robustness to adversarial perturbations. 804-813


James Thewlis, Hakan Bilen, Andrea Vedaldi:
Unsupervised learning of object frames by dense equivariant image labelling. 844-855
Daniel Milstein, Jason Pacheco, Leigh J. Hochberg, John D. Simeral, Beata Jarosiewicz, Erik B. Sudderth:
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. 868-878
Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S. Yu:
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. 879-888
Jaime S. Ide, Fabio Augusto Cappabianco, Fábio Augusto Faria, Chiang-shan R. Li:
Detrended Partial Cross Correlation for Brain Connectivity Analysis. 889-897
Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause:
Safe Model-based Reinforcement Learning with Stability Guarantees. 908-919


Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris:
GP CaKe: Effective brain connectivity with causal kernels. 951-960
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter:
Self-Normalizing Neural Networks. 972-981
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller:
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. 992-1002
Haw-Shiuan Chang, Erik G. Learned-Miller, Andrew McCallum:
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples. 1003-1013
William L. Hamilton, Zhitao Ying, Jure Leskovec:
Inductive Representation Learning on Large Graphs. 1025-1035
Ehsan Elhamifar, M. Clara De Paolis Kaluza:
Subset Selection and Summarization in Sequential Data. 1036-1045
Songbai Yan, Chicheng Zhang:
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces. 1056-1066
Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. 1067-1077
Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya:
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. 1078-1086
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. 1087-1098
Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort:
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding. 1099-1108
Damien Scieur, Vincent Roulet, Francis R. Bach, Alexandre d'Aspremont:
Integration Methods and Optimization Algorithms. 1109-1118
Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. 1130-1140
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc J. Van Gool:
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. 1141-1151
Stéphanie Allassonnière, Juliette Chevallier, Stephane Oudard:
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data. 1152-1160
Qinshi Wang, Wei Chen:
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications. 1161-1171
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell:
Predictive-State Decoders: Encoding the Future into Recurrent Networks. 1172-1183
Shipra Agrawal, Randy Jia:
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. 1184-1194
Antti Tarvainen, Harri Valpola:
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. 1195-1204
Nikolay Savinov, Lubor Ladicky, Marc Pollefeys:
Matching neural paths: transfer from recognition to correspondence search. 1205-1214
Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön:
Linearly constrained Gaussian processes. 1215-1224
Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher:
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data. 1225-1234
Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav S. Sukhatme, Joseph J. Lim:
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. 1235-1245
Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani:
Learning to Inpaint for Image Compression. 1246-1255
Anirban Roychowdhury, Srinivasan Parthasarathy:
Adaptive Bayesian Sampling with Monte Carlo EM. 1256-1266
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. 1267-1277
Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill Freeman:
Shape and Material from Sound. 1278-1288
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke:
Flexible statistical inference for mechanistic models of neural dynamics. 1289-1299

Kartik Ahuja, William R. Zame, Mihaela van der Schaar:
DPSCREEN: Dynamic Personalized Screening. 1321-1332
Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain:
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach. 1333-1342
Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic:
Testing and Learning on Distributions with Symmetric Noise Invariance. 1343-1353
Hongteng Xu, Hongyuan Zha:
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering. 1354-1363

Jason Xu, Eric C. Chi, Kenneth Lange:
Generalized Linear Model Regression under Distance-to-set Penalties. 1385-1394
Benjamin R. Cowley, Ryan C. Williamson, Katerina Clemens, Matthew A. Smith, Byron M. Yu:
Adaptive stimulus selection for optimizing neural population responses. 1395-1405
Emmanuel Abbe, Sanjeev R. Kulkarni, Eun Jee Lee:
Nonbacktracking Bounds on the Influence in Independent Cascade Models. 1406-1415
Hao Yu, Michael J. Neely, Xiaohan Wei:
Online Convex Optimization with Stochastic Constraints. 1427-1437
Dipan K. Pal, Ashwin A. Kannan, Gautam Arakalgud, Marios Savvides:
Max-Margin Invariant Features from Transformed Unlabelled Data. 1438-1446
Xiaoqian Wang, Hong Chen, Weidong Cai, Dinggang Shen, Heng Huang:
Regularized Modal Regression with Applications in Cognitive Impairment Prediction. 1447-1457
Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang:
Translation Synchronization via Truncated Least Squares. 1458-1467
Cheng Li, Felix Ming Fai Wong, Zhenming Liu, Varun Kanade:
From which world is your graph. 1468-1478


Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li:
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning. 1508-1518
Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz:
Learning Affinity via Spatial Propagation Networks. 1519-1529
Fei Xia, Martin J. Zhang, James Y. Zou, David Tse:
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features. 1540-1549
Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler:
Cost efficient gradient boosting. 1550-1560
Aryeh Kontorovich, Sivan Sabato, Roi Weiss:
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions. 1572-1582
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu:
Learning Multiple Tasks with Multilinear Relationship Networks. 1593-1602

Alberto Bietti, Julien Mairal:
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure. 1622-1632
Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl:
Deep Learning with Topological Signatures. 1633-1643
Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song:
Predicting User Activity Level In Point Processes With Mass Transport Equation. 1644-1654
Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff:
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues. 1655-1664
Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Positive-Unlabeled Learning with Non-Negative Risk Estimator. 1674-1684
Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh:
Optimal Sample Complexity of M-wise Data for Top-K Ranking. 1685-1695
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic:
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. 1707-1718
Ziming Zhang, Matthew Brand:
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks. 1719-1728
Elad Hoffer, Itay Hubara, Daniel Soudry:
Train longer, generalize better: closing the generalization gap in large batch training of neural networks. 1729-1739
Urs Köster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William Constable, Oguz Elibol, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao:
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks. 1740-1750
Richard Combes, Stefan Magureanu, Alexandre Proutière:
Minimal Exploration in Structured Stochastic Bandits. 1761-1769
Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk:
Learned D-AMP: Principled Neural Network based Compressive Image Recovery. 1770-1781
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding. 1782-1792
Liangpeng Zhang, Ke Tang, Xin Yao:
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning. 1802-1812

Luigi Acerbi, Wei Ji:
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search. 1834-1844
Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Chordal Markov Networks via Branch and Bound. 1845-1855
Andres Muñoz Medina, Sergei Vassilvitskii:
Revenue Optimization with Approximate Bid Predictions. 1856-1864
Gang Wang, Georgios B. Giannakis, Yousef Saad, Jie Chen:
Solving Most Systems of Random Quadratic Equations. 1865-1875
Wei-Ning Hsu, Yu Zhang, James R. Glass:
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data. 1876-1887

Raymond Yeh, Jinjun Xiong, Wen-Mei W. Hwu, Minh Do, Alexander G. Schwing:
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts. 1909-1919
Lixin Fan:
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network. 1920-1929
Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. 1930-1941
Sergey Ioffe:
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models. 1942-1950
Jason Altschuler, Jonathan Weed, Philippe Rigollet:
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. 1961-1971
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil:
Consistent Multitask Learning with Nonlinear Output Relations. 1983-1993
Niladri S. Chatterji, Peter L. Bartlett:
Alternating minimization for dictionary learning with random initialization. 1994-2003
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Stabilizing Training of Generative Adversarial Networks through Regularization. 2015-2025
Le Fang, Fan Yang, Wen Dong, Tong Guan, Chunming Qiao:
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems. 2026-2036
Rowan McAllister, Carl Edward Rasmussen:
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. 2037-2046
Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli:
Compatible Reward Inverse Reinforcement Learning. 2047-2056
Aryan Mokhtari, Alejandro Ribeiro:
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization. 2057-2065
Jacob Devlin, Rudy R. Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. 2077-2085
Stéphanie van der Pas, Veronika Rocková:
Bayesian Dyadic Trees and Histograms for Regression. 2086-2096
Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Özcimder:
A graph-theoretic approach to multitasking. 2097-2106
Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar:
Consistent Robust Regression. 2107-2116
Zhongwen Xu, Joseph Modayil, Hado P. van Hasselt, André Barreto, David Silver, Tom Schaul:
Natural Value Approximators: Learning when to Trust Past Estimates. 2117-2125
Zelda E. Mariet, Suvrit Sra:
Elementary Symmetric Polynomials for Optimal Experimental Design. 2136-2145
Serhii Havrylov, Ivan Titov:
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols. 2146-2156
Francesco Orabona, Tatiana Tommasi:
Training Deep Networks without Learning Rates Through Coin Betting. 2157-2167

Surbhi Goel, Adam R. Klivans:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. 2189-2199
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos:
MMD GAN: Towards Deeper Understanding of Moment Matching Network. 2200-2210
Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse:
The Reversible Residual Network: Backpropagation Without Storing Activations. 2211-2221
Quentin Berthet, Vianney Perchet:
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe. 2222-2231
Futoshi Futami, Issei Sato, Masashi Sugiyama:
Expectation Propagation for t-Exponential Family Using q-Algebra. 2242-2251
Eleni Triantafillou, Richard S. Zemel, Raquel Urtasun:
Few-Shot Learning Through an Information Retrieval Lens. 2252-2262
Matthias Hein, Maksym Andriushchenko:
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. 2263-2273
Alejandro Newell, Zhiao Huang, Jia Deng:
Associative Embedding: End-to-End Learning for Joint Detection and Grouping. 2274-2284
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta:
Practical Locally Private Heavy Hitters. 2285-2293
Kinjal Basu, Ankan Saha, Shaunak Chatterjee:
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences. 2294-2304
Fan Yang, Zhilin Yang, William W. Cohen:
Differentiable Learning of Logical Rules for Knowledge Base Reasoning. 2316-2325
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks. 2326-2334
George Papamakarios, Iain Murray, Theo Pavlakou:
Masked Autoregressive Flow for Density Estimation. 2335-2344
Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan:
Non-convex Finite-Sum Optimization Via SCSG Methods. 2345-2355
Rebecca E. Morrison, Ricardo Baptista, Youssef Marzouk:
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting. 2356-2366
Kai Fan, Qi Wei, Lawrence Carin, Katherine A. Heller:
An inner-loop free solution to inverse problems using deep neural networks. 2367-2377
Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis:
OnACID: Online Analysis of Calcium Imaging Data in Real Time. 2378-2388
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. 2399-2408
Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Scalable Demand-Aware Recommendation. 2409-2418
Yichong Xu, Hongyang Zhang, Aarti Singh, Artur Dubrawski, Kyle Miller:
Noise-Tolerant Interactive Learning Using Pairwise Comparisons. 2428-2437
Yonatan Belinkov, James R. Glass:
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems. 2438-2448
Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank C. Park, Daniel D. Lee:
Generative Local Metric Learning for Kernel Regression. 2449-2459
Linus Hamilton, Frederic Koehler, Ankur Moitra:
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. 2460-2469

Zhaohan Guo, Philip S. Thomas, Emma Brunskill:
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. 2489-2498
Amirhossein Taghvaei, Jin W. Kim, Prashant G. Mehta:
How regularization affects the critical points in linear networks. 2499-2509
Aolin Xu, Maxim Raginsky:
Information-theoretic analysis of generalization capability of learning algorithms. 2521-2530
Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter:
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems. 2542-2551
Chengxu Zhuang, Jonas Kubilius, Mitra J. Z. Hartmann, Daniel L. Yamins:
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. 2552-2562
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. 2563-2573
Justin Fu, John D. Co-Reyes, Sergey Levine:
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning. 2574-2584
Guillaume Rabusseau, Borja Balle, Joelle Pineau:
Multitask Spectral Learning of Weighted Automata. 2585-2594
Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou:
Multi-way Interacting Regression via Factorization Machines. 2595-2603
Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola:
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. 2604-2613
Piotr Indyk, Ilya P. Razenshteyn, Tal Wagner:
Practical Data-Dependent Metric Compression with Provable Guarantees. 2614-2623
George Tucker, Andriy Mnih, Chris J. Maddison, John Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. 2624-2633
Matthew Staib, Sebastian Claici, Justin M. Solomon, Stefanie Jegelka:
Parallel Streaming Wasserstein Barycenters. 2644-2655
Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick:
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games. 2656-2666
Tu Nguyen, Trung Le, Hung Vu, Dinh Q. Phung:
Dual Discriminator Generative Adversarial Nets. 2667-2677
Santiago R. Balseiro, Max Lin, Vahab S. Mirrokni, Renato Paes Leme, Song Zuo:
Dynamic Revenue Sharing. 2678-2686
Mohammad Ali Bashiri, Xinhua Zhang:
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search. 2687-2697


Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John William Paisley, David M. Blei:
Variational Inference via \chi Upper Bound Minimization. 2729-2738
Xingguo Li, Lin Yang, Jason Ge, Jarvis D. Haupt, Tong Zhang, Tuo Zhao:
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning. 2739-2749
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. 2750-2759
Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha:
An Empirical Study on The Properties of Random Bases for Kernel Methods. 2760-2771
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Bridging the Gap Between Value and Policy Based Reinforcement Learning. 2772-2782
Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng:
Premise Selection for Theorem Proving by Deep Graph Embedding. 2783-2793
Toan Tran, Trung Pham, Gustavo Carneiro, Lyle J. Palmer, Ian D. Reid:
A Bayesian Data Augmentation Approach for Learning Deep Models. 2794-2803


Sheng Chen, Arindam Banerjee:
Alternating Estimation for Structured High-Dimensional Multi-Response Models. 2835-2844
Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. 2845-2854
Xiaohan Wei, Stanislav Minsker:
Estimation of the covariance structure of heavy-tailed distributions. 2855-2864
Alina Ene, Huy L. Nguyen, László A. Végh:
Decomposable Submodular Function Minimization: Discrete and Continuous. 2874-2884
Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon:
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs. 2895-2904
Hakan Inan, Murat A. Erdogdu, Mark J. Schnitzer:
Robust Estimation of Neural Signals in Calcium Imaging. 2905-2914

Ben London:
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent. 2935-2944
Roel Dobbe, David Fridovich-Keil, Claire Tomlin:
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach. 2945-2954
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Model-Powered Conditional Independence Test. 2955-2965
Andrew Gibiansky, Sercan Ömer Arik, Gregory Frederick Diamos, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou:
Deep Voice 2: Multi-Speaker Neural Text-to-Speech. 2966-2974
Seungil You, David Ding, Kevin Robert Canini, Jan Pfeifer, Maya R. Gupta:
Deep Lattice Networks and Partial Monotonic Functions. 2985-2993
Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim:
Continual Learning with Deep Generative Replay. 2994-3003
Marco F. Cusumano-Towner, Vikash K. Mansinghka:
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. 3004-3014
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. 3015-3025
Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani:
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback. 3026-3036
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon:
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem. 3037-3045

Nicolò Colombo, Ricardo Silva, Soong Moon Kang:
Tomography of the London Underground: a Scalable Model for Origin-Destination Data. 3065-3076
Michal Derezinski, Manfred K. Warmuth:
Unbiased estimates for linear regression via volume sampling. 3087-3096
Benjamin Moseley, Joshua Wang:
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. 3097-3106
Mingrui Liu, Tianbao Yang:
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. 3107-3117

Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Shallow Updates for Deep Reinforcement Learning. 3138-3148
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu:
LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 3149-3157
Kevin Lin, Dianqi Li, Xiaodong He, Ming-Ting Sun, Zhengyou Zhang:
Adversarial Ranking for Language Generation. 3158-3168
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill:
Regret Minimization in MDPs with Options without Prior Knowledge. 3169-3179
Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg:
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee. 3180-3189
Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo:
Graph Matching via Multiplicative Update Algorithm. 3190-3198
Qi Lou, Rina Dechter, Alexander T. Ihler:
Dynamic Importance Sampling for Anytime Bounds of the Partition Function. 3199-3207
Alessandro Rudi, Lorenzo Rosasco:
Generalization Properties of Learning with Random Features. 3218-3228
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela:
Differentially private Bayesian learning on distributed data. 3229-3238
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré:
Learning to Compose Domain-Specific Transformations for Data Augmentation. 3239-3249
Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Xiaokang Yang, Le Song, Hongyuan Zha:
Wasserstein Learning of Deep Generative Point Process Models. 3250-3259

Yi Xu, Qihang Lin, Tianbao Yang:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. 3279-3289
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner:
Streaming Sparse Gaussian Process Approximations. 3301-3309
Akash Srivastava, Lazar Valkoz, Chris Russell, Michael U. Gutmann, Charles A. Sutton:
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. 3310-3320

Vlad Niculae, Mathieu Blondel:
A Regularized Framework for Sparse and Structured Neural Attention. 3340-3350
Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda:
Multi-output Polynomial Networks and Factorization Machines. 3351-3361
Cyrus Rashtchian, Konstantin Makarychev, Miklós Z. Rácz, Siena Ang, Djordje Jevdjic, Sergey Yekhanin, Luis Ceze, Karin Strauss:
Clustering Billions of Reads for DNA Data Storage. 3362-3373
Ashkan Panahi, Babak Hassibi:
A Universal Analysis of Large-Scale Regularized Least Squares Solutions. 3384-3393
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan R. Salakhutdinov, Alexander J. Smola:
Deep Sets. 3394-3404
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Chris Pal:
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. 3405-3416
Pratibha Vellanki, Santu Rana, Sunil Kumar Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul G. Sanders, Svetha Venkatesh:
Process-constrained batch Bayesian optimisation. 3417-3426
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes. 3427-3435
Wouter Boomsma, Jes Frellsen:
Spherical convolutions and their application in molecular modelling. 3436-3446
Wen-bing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun, Junzhou Huang:
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding. 3447-3457
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh:
On Optimal Generalizability in Parametric Learning. 3458-3468
Xingguo Li, Jarvis D. Haupt, David P. Woodruff:
Near Optimal Sketching of Low-Rank Tensor Regression. 3469-3479
Laurence Aitchison, Lloyd Russell, Adam M. Packer, Jinyao Yan, Philippe Castonguay, Michael Häusser, Srinivas C. Turaga:
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit. 3489-3498
Anqi Wu, Nicholas G. Roy, Stephen Keeley, Jonathan W. Pillow:
Gaussian process based nonlinear latent structure discovery in multivariate spike train data. 3499-3508
David Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge:
Neural system identification for large populations separating "what" and "where". 3509-3519
Jacob Steinhardt, Pang Wei Koh, Percy S. Liang:
Certified Defenses for Data Poisoning Attacks. 3520-3532
Alexander Berardino, Valero Laparra, Johannes Ballé, Eero P. Simoncelli:
Eigen-Distortions of Hierarchical Representations. 3533-3542
Yossi Arjevani:
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization. 3543-3552
Yu Liu, Jianshu Chen, Li Deng:
Unsupervised Sequence Classification using Sequential Output Statistics. 3553-3562


Matteo Papini, Matteo Pirotta, Marcello Restelli:
Adaptive Batch Size for Safe Policy Gradients. 3594-3603
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther:
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. 3604-3613
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick:
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. 3614-3624
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. 3635-3645
Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel:
A multi-agent reinforcement learning model of common-pool resource appropriation. 3646-3655
Idan Schwartz, Alexander G. Schwing, Tamir Hazan:
High-Order Attention Models for Visual Question Answering. 3667-3677
Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht:
Sparse convolutional coding for neuronal assembly detection. 3678-3688
Giuseppe Pica, Eugenio Piasini, Houman Safaai, Caroline Runyan, Christopher D. Harvey, Mathew E. Diamond, Christoph Kayser, Tommaso Fellin, Stefano Panzeri:
Quantifying how much sensory information in a neural code is relevant for behavior. 3689-3699
Federico Monti, Michael M. Bronstein, Xavier Bresson:
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. 3700-3710
Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams:
Reducing Reparameterization Gradient Variance. 3711-3721
Paul Hongsuck Seo, Andreas Lehrmann, Bohyung Han, Leonid Sigal:
Visual Reference Resolution using Attention Memory for Visual Dialog. 3722-3732
Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy:
Joint distribution optimal transportation for domain adaptation. 3733-3742
Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler:
Multiresolution Kernel Approximation for Gaussian Process Regression. 3743-3751
Boqian Zhang, Jiangwei Pan, Vinayak A. Rao:
Collapsed variational Bayes for Markov jump processes. 3752-3760
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet:
Universal consistency and minimax rates for online Mondrian Forests. 3761-3770
Siyuan Ma, Mikhail Belkin:
Diving into the shallows: a computational perspective on large-scale shallow learning. 3781-3790
Qiang Li, Wei Chen, Xiaoming Sun, Jialin Zhang:
Influence Maximization with ε-Almost Submodular Threshold Functions. 3804-3814
Yunzhu Li, Jiaming Song, Stefano Ermon:
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. 3815-3825

Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. 3849-3858
Di Kang, Debarun Dhar, Antoni B. Chan:
Incorporating Side Information by Adaptive Convolution. 3870-3880
Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen:
Conic Scan-and-Cover algorithms for nonparametric topic modeling. 3881-3890
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco:
FALKON: An Optimal Large Scale Kernel Method. 3891-3901
Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. 3902-3912
Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi, Benjamin Van Roy:
Conservative Contextual Linear Bandits. 3913-3922
Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende:
Variational Memory Addressing in Generative Models. 3923-3932
Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi:
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm. 3933-3942
Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson:
Scalable Levy Process Priors for Spectral Kernel Learning. 3943-3952
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhen Liu, Bo Dai, Tuo Zhao, Le Song:
Deep Hyperspherical Learning. 3953-3963
Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe:
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction. 3964-3973
Graham Neubig, Yoav Goldberg, Chris Dyer:
On-the-fly Operation Batching in Dynamic Computation Graphs. 3974-3984
Damien Scieur, Francis R. Bach, Alexandre d'Aspremont:
Nonlinear Acceleration of Stochastic Algorithms. 3985-3994
Flávio du Pin Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Optimized Pre-Processing for Discrimination Prevention. 3995-4004
Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski:
YASS: Yet Another Spike Sorter. 4005-4015
Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke:
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. 4027-4037
Ethan R. Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi:
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly. 4047-4057
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado P. van Hasselt:
Successor Features for Transfer in Reinforcement Learning. 4058-4068


Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation. 4102-4111
Mali Sundaresan, Arshed Nabeel, Devarajan Sridharan:
Mapping distinct timescales of functional interactions among brain networks. 4112-4121
Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang:
Learning A Structured Optimal Bipartite Graph for Co-Clustering. 4132-4141
Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht:
The Marginal Value of Adaptive Gradient Methods in Machine Learning. 4151-4161
Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Franck Iutzeler, Yury Maximov:
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification. 4162-4171
Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin:
Deconvolutional Paragraph Representation Learning. 4172-4182
Wojciech Kotlowski, Wouter M. Koolen, Alan Malek:
Random Permutation Online Isotonic Regression. 4183-4192
Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel:
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. 4193-4206
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy, Bo Wahlberg:
Inverse Filtering for Hidden Markov Models. 4207-4216

Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin:
VAE Learning via Stein Variational Gradient Descent. 4239-4248
Yagmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel A. J. van Gerven:
Reconstructing perceived faces from brain activations with deep adversarial neural decoding. 4249-4260
Celestine Dünner, Thomas P. Parnell, Martin Jaggi:
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems. 4261-4270
Prateep Bhattacharjee, Sukhendu Das:
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks. 4271-4280
Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, Razvan Pascanu:
Sobolev Training for Neural Networks. 4281-4290
Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei:
Deep Reinforcement Learning from Human Preferences. 4302-4310
Arturs Backurs, Piotr Indyk, Ludwig Schmidt:
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks. 4311-4321
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Policy Gradient With Value Function Approximation For Collective Multiagent Planning. 4322-4332
Yunchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin:
Adversarial Symmetric Variational Autoencoder. 4333-4342
Cesar F. Caiafa, Olaf Sporns, Andrew J. Saykin, Franco Pestilli:
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays. 4343-4354
Emmanouil Antonios Platanios, Hoifung Poon, Tom M. Mitchell, Eric Joel Horvitz:
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach. 4364-4373
Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan:
A Decomposition of Forecast Error in Prediction Markets. 4374-4383
Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio:
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. 4395-4405
Qian Yu, Mohammad Ali Maddah-Ali, Salman Avestimehr:
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication. 4406-4416
Emily L. Denton, Vighnesh Birodkar:
Unsupervised Learning of Disentangled Representations from Video. 4417-4426
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S. Talwalkar:
Federated Multi-Task Learning. 4427-4437
Cameron Musco, David P. Woodruff:
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? 4438-4448
Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar:
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. 4449-4459
Dominique Joncas, Marina Meila, James McQueen:
Improved Graph Laplacian via Geometric Self-Consistency. 4460-4469
Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng:
Dual Path Networks. 4470-4478
Cong Fang, Feng Cheng, Zhouchen Lin:
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers. 4479-4488
Qinliang Su, Xuejun Liao, Lawrence Carin:
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks. 4489-4498
Yee Whye Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu:
Distral: Robust multitask reinforcement learning. 4499-4509
M. Sevi Baltaoglu, Lang Tong, Qing Zhao:
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions. 4510-4520
Ingmar Kanitscheider, Ila Fiete:
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems. 4532-4541
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, Andrea Tacchetti:
Visual Interaction Networks: Learning a Physics Simulator from Video. 4542-4550
Slobodan Mitrovic, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub Tarnawski, Volkan Cevher:
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach. 4560-4569
Ping Li, Martin Slawski:
Simple strategies for recovering inner products from coarsely quantized random projections. 4570-4579
Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Discovering Potential Correlations via Hypercontractivity. 4580-4590
Hugh Salimbeni, Marc Peter Deisenroth:
Doubly Stochastic Variational Inference for Deep Gaussian Processes. 4591-4602
Stéphan Clémençon, Mastane Achab:
Ranking Data with Continuous Labels through Oriented Recursive Partitions. 4603-4611
Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin:
Scalable Model Selection for Belief Networks. 4612-4622
Yitong Li, michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson:
Targeting EEG/LFP Synchrony with Neural Nets. 4623-4633
Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer:
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs. 4634-4644
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang:
Overcoming Catastrophic Forgetting by Incremental Moment Matching. 4655-4665
Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti:
Balancing information exposure in social networks. 4666-4674
Zahra Ghodsi, Tianyu Gu, Siddharth Garg:
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud. 4675-4684
Péter Karkus, David Hsu, Wee Sun Lee:
QMDP-Net: Deep Learning for Planning under Partial Observability. 4697-4707
Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis:
Robust Optimization for Non-Convex Objectives. 4708-4717
Christian Borgs, Jennifer T. Chayes, Christina E. Lee, Devavrat Shah:
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation. 4718-4729
Linxi Liu, Dangna Li, Wing Hung Wong:
Convergence rates of a partition based Bayesian multivariate density estimation method. 4741-4749
Tomer Koren, Roi Livni:
Affine-Invariant Online Optimization and the Low-rank Experts Problem. 4750-4758
Omar El Housni, Vineet Goyal:
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization. 4759-4767
Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro:
Stochastic Approximation for Canonical Correlation Analysis. 4778-4787
Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. 4788-4798
Maria-Florina Balcan, Hongyang Zhang:
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions. 4799-4808
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann:
Scalable Variational Inference for Dynamical Systems. 4809-4818
Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Context Selection for Embedding Models. 4819-4828
Anastasya Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas:
Working hard to know your neighbor's margins: Local descriptor learning loss. 4829-4840
Chao-Bing Song, Shaobo Cui, Yong Jiang, Shu-Tao Xia:
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex. 4841-4850
Aniket Anand Deshmukh, Ürün Dogan, Clayton Scott:
Multi-Task Learning for Contextual Bandits. 4851-4859
Xin Dong, Shangyu Chen, Sinno Jialin Pan:
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon. 4860-4874
Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao:
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. 4875-4884


Chao Pan, Michael Zhu:
Group Additive Structure Identification for Kernel Nonparametric Regression. 4914-4923
Gauri Jagatap, Chinmay Hegde:
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval. 4924-4934
Dan Svenstrup, Jonas Meinertz Hansen, Ole Winther:
Hash Embeddings for Efficient Word Representations. 4935-4943
Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash:
Online Learning for Multivariate Hawkes Processes. 4944-4953
Maksims Volkovs, Guang Wei Yu, Tomi Poutanen:
DropoutNet: Addressing Cold Start in Recommender Systems. 4964-4973
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Tim Lillicrap:
A simple neural network module for relational reasoning. 4974-4983
Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng:
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes. 4984-4993
Junpei Komiyama, Junya Honda, Akiko Takeda:
Position-based Multiple-play Bandit Problem with Unknown Position Bias. 5005-5015
Garrett Andersen, George Konidaris:
Active Exploration for Learning Symbolic Representations. 5016-5026
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet:
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling. 5027-5035
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Fair Clustering Through Fairlets. 5036-5044
Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Polynomial time algorithms for dual volume sampling. 5045-5054
Marcin Andrychowicz, Dwight Crow, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba:
Hindsight Experience Replay. 5055-5065
Ashok Cutkosky, Kwabena A. Boahen:
Stochastic and Adversarial Online Learning without Hyperparameters. 5066-5074
Huan Ling, Sanja Fidler:
Teaching Machines to Describe Images with Natural Language Feedback. 5075-5085
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. 5086-5094
Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. 5095-5104
Charles Ruizhongtai Qi, Li Yi, Hao Su, Leonidas J. Guibas:
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. 5105-5114
Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han:
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization. 5115-5124
Alberto García-Durán, Mathias Niepert:
Learning Graph Representations with Embedding Propagation. 5125-5136
Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence:
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. 5137-5145
Rishit Sheth, Roni Khardon:
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models. 5157-5167
Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang:
Saliency-based Sequential Image Attention with Multiset Prediction. 5179-5189
Ching-An Cheng, Byron Boots:
Variational Inference for Gaussian Process Models with Linear Complexity. 5190-5200



Hsiao-Yu Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki:
Self-supervised Learning of Motion Capture. 5242-5252
Zhe Gan, Liqun Chen, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Hao Liu, Chunyuan Li, Lawrence Carin:
Triangle Generative Adversarial Networks. 5253-5262
Yi-An Lai, Chin-Chi Hsu, Wen-Hao Chen, Mi-Yen Yeh, Shou-De Lin:
PRUNE: Preserving Proximity and Global Ranking for Network Embedding. 5263-5272
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier:
Bayesian Optimization with Gradients. 5273-5284
Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba:
Second-order Optimization for Deep Reinforcement Learning using Kronecker-factored Approximation. 5285-5294
Joseph Geumlek, Shuang Song, Kamalika Chaudhuri:
Renyi Differential Privacy Mechanisms for Posterior Sampling. 5295-5304
Ofer Dekel, Arthur Flajolet, Nika Haghtalab, Patrick Jaillet:
Online Learning with a Hint. 5305-5314
Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman:
Identification of Gaussian Process State Space Models. 5315-5325
Ziyu Wang, Josh S. Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess:
Robust Imitation of Diverse Behaviors. 5326-5335
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu:
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. 5336-5346
Vasilis Syrgkanis:
A Sample Complexity Measure with Applications to Learning Optimal Auctions. 5358-5365
Thomas Anthony, Zheng Tian, David Barber:
Thinking Fast and Slow with Deep Learning and Tree Search. 5366-5376
Yogatheesan Varatharajah, Min Jin Chong, Krishnakant Saboo, Brent M. Berry, Benjamin H. Brinkmann, Gregory A. Worrell, Ravishankar K. Iyer:
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms. 5377-5386
Harm van Seijen, Mehdi Fatemi, Romain Laroche, Joshua Romoff, Tavian Barnes, Jeffrey Tsang:
Hybrid Reward Architecture for Reinforcement Learning. 5398-5408
Luiz F. O. Chamon, Alejandro Ribeiro:
Approximate Supermodularity Bounds for Experimental Design. 5409-5418
Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz:
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. 5419-5429
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. 5430-5439
Can Karakus, Yifan Sun, Suhas N. Diggavi, Wotao Yin:
Straggler Mitigation in Distributed Optimization Through Data Encoding. 5440-5448
Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin:
Multi-View Decision Processes: The Helper-AI Problem. 5449-5458
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon:
A Greedy Approach for Budgeted Maximum Inner Product Search. 5459-5468
Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung:
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks. 5469-5479
Çaglar Gülçehre, Francis Dutil, Adam Trischler, Yoshua Bengio:
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models. 5480-5489
Priya L. Donti, J. Zico Kolter, Brandon Amos:
Task-based End-to-end Model Learning in Stochastic Optimization. 5490-5500
Chunyuan Li, Hao Liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin:
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching. 5501-5509
Yue Wang, Wei Chen, Yuting Liu, Zhiming Ma, Tie-Yan Liu:
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting. 5510-5519
Le Song, Santosh Vempala, John Wilmes, Bo Xie:
On the Complexity of Learning Neural Networks. 5520-5528
Dustin Tran, Rajesh Ranganath, David M. Blei:
Hierarchical Implicit Models and Likelihood-Free Variational Inference. 5529-5539
Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher:
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. 5540-5550
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. 5551-5559
Hao He, Bo Xin, Satoshi Ikehata, David P. Wipf:
From Bayesian Sparsity to Gated Recurrent Nets. 5560-5570
Christopher Srinivasa, Inmar E. Givoni, Siamak Ravanbakhsh, Brendan J. Frey:
Min-Max Propagation. 5571-5579
Alex Kendall, Yarin Gal:
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? 5580-5590
Arash Vahdat:
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks. 5601-5610
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. 5622-5632
Anna Volokitin, Gemma Roig, Tomaso A. Poggio:
Do Deep Neural Networks Suffer from Crowding? 5633-5643
Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama:
Learning from Complementary Labels. 5644-5654
Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan:
Online control of the false discovery rate with decaying memory. 5655-5664
Anton Mallasto, Aasa Feragen:
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes. 5665-5674
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger:
On Fairness and Calibration. 5684-5693
Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra:
Imagination-Augmented Agents for Deep Reinforcement Learning. 5694-5705
Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke:
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. 5706-5716
Christoph Dann, Tor Lattimore, Emma Brunskill:
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. 5717-5727
John T. Halloran, David M. Rocke:
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra. 5728-5737
Ofer Meshi, Alexander G. Schwing:
Asynchronous Parallel Coordinate Minimization for MAP Inference. 5738-5748
Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N. Holtmann-Rice, David Simcha, Felix X. Yu:
Multiscale Quantization for Fast Similarity Search. 5749-5757
Liwei Wang, Alexander G. Schwing, Svetlana Lazebnik:
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space. 5758-5768
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville:
Improved Training of Wasserstein GANs. 5769-5779

Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani:
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. 5802-5812
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein:
Training Quantized Nets: A Deeper Understanding. 5813-5823
Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler:
Permutation-based Causal Inference Algorithms with Interventions. 5824-5833
Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing:
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis. 5834-5842
S. Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi:
Gradient Methods for Submodular Maximization. 5843-5853
Ahmet Alacaoglu, Quoc Tran-Dinh, Olivier Fercoq, Volkan Cevher:
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization. 5854-5863
Eric Balkanski, Nicole Immorlica, Yaron Singer:
The Importance of Communities for Learning to Influence. 5864-5873
Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras:
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos. 5874-5884
Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux:
Learning Neural Representations of Human Cognition across Many fMRI Studies. 5885-5895
Ervin Tanczos, Robert Nowak, Bob Mankoff:
A KL-LUCB algorithm for Large-Scale Crowdsourcing. 5896-5905
Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar:
Collaborative Deep Learning in Fixed Topology Networks. 5906-5916
Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. 5927-5937
Michael Janner, Jiajun Wu, Tejas D. Kulkarni, Ilker Yildirim, Josh Tenenbaum:
Self-Supervised Intrinsic Image Decomposition. 5938-5948
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro:
Exploring Generalization in Deep Learning. 5949-5958
Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright:
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control. 5959-5968
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato:
Fader Networks: Manipulating Images by Sliding Attributes. 5969-5978
Kristjan Greenewald, Ambuj Tewari, Susan A. Murphy, Predrag V. Klasnja:
Action Centered Contextual Bandits. 5979-5987
Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Estimating Mutual Information for Discrete-Continuous Mixtures. 5988-5999
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin:
Attention is All you Need. 6000-6010
Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hao, Antti Rasmus, Rinu Boney, Harri Valpola:
Recurrent Ladder Networks. 6011-6021
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan:
Parameter-Free Online Learning via Model Selection. 6022-6032
Zhan Shi, Xinhua Zhang, Yaoliang Yu:
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. 6033-6043
Edouard Grave, Moustapha Cissé, Armand Joulin:
Unbounded cache model for online language modeling with open vocabulary. 6044-6054
Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li:
Predictive State Recurrent Neural Networks. 6055-6066
Yuting Wei, Fanny Yang, Martin J. Wainwright:
Early stopping for kernel boosting algorithms: A general analysis with localized complexities. 6067-6077
Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. 6078-6087
Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu:
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. 6099-6108

Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang:
A Learning Error Analysis for Structured Prediction with Approximate Inference. 6131-6141
Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Efficient Second-Order Online Kernel Learning with Adaptive Embedding. 6142-6151
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Implicit Regularization in Matrix Factorization. 6152-6160
Danny Barash, Matan Gavish:
Optimal Shrinkage of Singular Values Under Random Data Contamination. 6161-6171
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin:
Countering Feedback Delays in Multi-Agent Learning. 6172-6182
Tao Sun, Robert Hannah, Wotao Yin:
Asynchronous Coordinate Descent under More Realistic Assumptions. 6183-6191
Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li:
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls. 6192-6201
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Hierarchical Clustering Beyond the Worst-Case. 6202-6210
Alberto Bietti, Julien Mairal:
Invariance and Stability of Deep Convolutional Representations. 6211-6221
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang:
The Expressive Power of Neural Networks: A View from the Width. 6232-6240
Peter L. Bartlett, Dylan J. Foster, Matus J. Telgarsky:
Spectrally-normalized margin bounds for neural networks. 6241-6250
Taylor W. Killian, Samuel Daulton, Finale Doshi-Velez, George Konidaris:
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. 6251-6262
Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu:
Population Matching Discrepancy and Applications in Deep Learning. 6263-6275
Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. 6276-6286
Nicolò Cesa-Bianchi, Claudio Gentile, Gergely Neu, Gábor Lugosi:
Boltzmann Exploration Done Right. 6287-6296
Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher:
Learned in Translation: Contextualized Word Vectors. 6297-6308
Aäron van den Oord, Oriol Vinyals, Koray Kavukcuoglu:
Neural Discrete Representation Learning. 6309-6318
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Wilson:
Scalable Log Determinants for Gaussian Process Kernel Learning. 6330-6340
Maximilian Nickel, Douwe Kiela:
Poincaré Embeddings for Learning Hierarchical Representations. 6341-6350
Elias B. Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, Le Song:
Learning Combinatorial Optimization Algorithms over Graphs. 6351-6361
Amélie Héliou, Johanne Cohen, Panayotis Mertikopoulos:
Learning with Bandit Feedback in Potential Games. 6372-6381
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch:
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. 6382-6393
Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt:
Communication-Efficient Distributed Learning of Discrete Distributions. 6394-6404
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell:
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. 6405-6416
Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva:
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. 6417-6426
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski:
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons. 6437-6448
Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. 6449-6459
Asish Ghoshal, Jean Honorio:
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity. 6460-6469
Michael Kamp, Mario Boley, Olana Missura, Thomas Gärtner:
Effective Parallelisation for Machine Learning. 6480-6491
Arya Mazumdar, Soumyabrata Pal:
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding. 6492-6502
Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang:
Clustering Stable Instances of Euclidean k-means. 6503-6512
Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov:
Good Semi-supervised Learning That Requires a Bad GAN. 6513-6523
Krzysztof Marcin Choromanski, Vikas Sindhwani:
On Blackbox Backpropagation and Jacobian Sensing. 6524-6532
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur:
Protein Interface Prediction using Graph Convolutional Networks. 6533-6542
Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat:
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities. 6543-6552
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade:
Towards Generalization and Simplicity in Continuous Control. 6553-6564
Amir-massoud Farahmand, Sepideh Pourazarm, Daniel Nikovski:
Random Projection Filter Bank for Time Series Data. 6565-6575
Chris J. Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. 6576-6586
Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville:
Modulating early visual processing by language. 6597-6607
Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy:
Learning Mixture of Gaussians with Streaming Data. 6608-6617
Søren Dahlgaard, Mathias Bæk Tejs Knudsen, Mikkel Thorup:
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction. 6618-6628
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter:
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. 6629-6640
Chuang Wang, Yue M. Lu:
The Scaling Limit of High-Dimensional Online Independent Component Analysis. 6641-6650
Karl Bringmann, Pavel Kolev, David P. Woodruff:
Approximation Algorithms for l0-Low Rank Approximation. 6651-6662
Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati:
The power of absolute discounting: all-dimensional distribution estimation. 6663-6672
Saeid Motiian, Quinn Jones, Seyed Mehdi Iranmanesh, Gianfranco Doretto:
Few-Shot Adversarial Domain Adaptation. 6673-6683
Gabriel Parra, Felipe Tobar:
Spectral Mixture Kernels for Multi-Output Gaussian Processes. 6684-6693
Elad Hazan, Karan Singh, Cyril Zhang:
Learning Linear Dynamical Systems via Spectral Filtering. 6705-6715
Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio:
Z-Forcing: Training Stochastic Recurrent Networks. 6716-6726
Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess:
Learning Hierarchical Information Flow with Recurrent Neural Modules. 6727-6736
Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. 6737-6746
Hongyuan Mei, Jason Eisner:
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process. 6757-6767
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca D. Dragan:
Inverse Reward Design. 6768-6777
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. 6778-6787
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin. 6788-6798
Walid Krichene, Peter L. Bartlett:
Acceleration and Averaging in Stochastic Descent Dynamics. 6799-6809
Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung:
An Error Detection and Correction Framework for Connectomics. 6821-6832
Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Style Transfer from Non-Parallel Text by Cross-Alignment. 6833-6844
Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson:
Cross-Spectral Factor Analysis. 6845-6855
Mohammad Reza Karimi, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Stochastic Submodular Maximization: The Case of Coverage Functions. 6856-6866
MohammadHossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab S. Mirrokni:
Affinity Clustering: Hierarchical Clustering at Scale. 6867-6877
Tatsunori B. Hashimoto, Percy S. Liang, John C. Duchi:
Unsupervised Transformation Learning via Convex Relaxations. 6878-6886
Kevin Lin, James Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani:
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. 6887-6896
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle:
A Meta-Learning Perspective on Cold-Start Recommendations for Items. 6907-6917
Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin, Yunpeng Chen, Zequn Jie, Jiashi Feng, Shuicheng Yan:
Predicting Scene Parsing and Motion Dynamics in the Future. 6918-6927
Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud:
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. 6928-6937
Muhammad Farhan, Juvaria Tariq, Arif Zaman, Mudassir Shabbir, Imdadullah Khan:
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification. 6938-6948
Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan:
Kernel Feature Selection via Conditional Covariance Minimization. 6949-6958
Bowei Yan, Mingzhang Yin, Purnamrita Sarkar:
Convergence of Gradient EM on Multi-component Mixture of Gaussians. 6959-6969
Moustapha Cissé, Yossi Adi, Natalia Neverova, Joseph Keshet:
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples. 6980-6990
Stefan Bauer, Nico S. Gorbach, Djordje Miladinovic, Joachim M. Buhmann:
Efficient and Flexible Inference for Stochastic Systems. 6991-7001
Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli:
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent. 7002-7010
Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Experimental Design for Learning Causal Graphs with Latent Variables. 7021-7031
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen P. Boyd, Peter W. Glynn:
Stochastic Mirror Descent in Variationally Coherent Optimization Problems. 7043-7052
Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep Ravikumar:
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models. 7053-7062
Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar:
Maxing and Ranking with Few Assumptions. 7063-7073
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin:
On clustering network-valued data. 7074-7084
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvári:
Multi-view Matrix Factorization for Linear Dynamical System Estimation. 7095-7104



Google
Google Scholar
MS Academic
CiteSeerX
CORE
Semantic Scholar
